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A methodology to select topology generators for ad hoc mesh network simulations

A methodology to select topology generators for ad hoc mesh network simulations
A methodology to select topology generators for ad hoc mesh network simulations
Many academic and industrial research working on Wireless Communications and Networking rely on simulations, at least in the first stages, to obtain preliminary results to be subsequently validated in real settings. Topology generators (TG) are commonly used to generate the initial placement of nodes in artificial Ad Hoc Mesh Network topologies, where those simulations take place. The significance of these experiments heavily depends on the representativeness of artificial topologies. Indeed, if they were not drawn fairly, obtained results would apply only to a subset of possible configurations, hence they would lack of the appropriate generality required to port them to the real world. Although using many TGs could mitigate this issue by generating topologies in several different ways, which would entail a significant additional effort. Hence, the problem arises of what TGs to choose, among a number of available generators, to maximise the representativeness of generated topologies and reduce the number of TGs to use.
In this paper, we address this problem by investigating the presence of bias in the initial placement of nodes in artificial Ad Hoc Mesh Network topologies produced by different TGs. Indeed, any bias introduced by some TGs would affect the representativeness of generated topologies. We propose a methodology to quantify this bias and select what TGs to employ to minimise it, given the number of TGs to use. Our methodology relies on the extraction of a number of features from generated topologies. We carry out experiments on three well-known TGs, namely BRITE, NPART and GT-ITM. We find that selecting NPART (if one TG needs to be chosen) or BRITE and NPART (if two TGs need to be chosen) allows to minimise the bias index. The impact of our research lies in the provisioning of an empirical methodology to select the TGs to use for Ad Hoc Mesh Network simulations, which is expected to be beneficial for researchers involved in experimental evaluations on this type of networks.
1796-2021
O'Sullivan, Michael
a100f402-63c5-4fe7-b817-fc1c9da31097
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3
Sassone, Vladimiro
df7d3c83-2aa0-4571-be94-9473b07b03e7
O'Sullivan, Michael
a100f402-63c5-4fe7-b817-fc1c9da31097
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3
Sassone, Vladimiro
df7d3c83-2aa0-4571-be94-9473b07b03e7

O'Sullivan, Michael, Aniello, Leonardo and Sassone, Vladimiro (2020) A methodology to select topology generators for ad hoc mesh network simulations. Journal of Communications. (In Press)

Record type: Article

Abstract

Many academic and industrial research working on Wireless Communications and Networking rely on simulations, at least in the first stages, to obtain preliminary results to be subsequently validated in real settings. Topology generators (TG) are commonly used to generate the initial placement of nodes in artificial Ad Hoc Mesh Network topologies, where those simulations take place. The significance of these experiments heavily depends on the representativeness of artificial topologies. Indeed, if they were not drawn fairly, obtained results would apply only to a subset of possible configurations, hence they would lack of the appropriate generality required to port them to the real world. Although using many TGs could mitigate this issue by generating topologies in several different ways, which would entail a significant additional effort. Hence, the problem arises of what TGs to choose, among a number of available generators, to maximise the representativeness of generated topologies and reduce the number of TGs to use.
In this paper, we address this problem by investigating the presence of bias in the initial placement of nodes in artificial Ad Hoc Mesh Network topologies produced by different TGs. Indeed, any bias introduced by some TGs would affect the representativeness of generated topologies. We propose a methodology to quantify this bias and select what TGs to employ to minimise it, given the number of TGs to use. Our methodology relies on the extraction of a number of features from generated topologies. We carry out experiments on three well-known TGs, namely BRITE, NPART and GT-ITM. We find that selecting NPART (if one TG needs to be chosen) or BRITE and NPART (if two TGs need to be chosen) allows to minimise the bias index. The impact of our research lies in the provisioning of an empirical methodology to select the TGs to use for Ad Hoc Mesh Network simulations, which is expected to be beneficial for researchers involved in experimental evaluations on this type of networks.

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A Methodology to Select Topology Generators for AdHoc Mesh Network (camera ready) - Accepted Manuscript
Restricted to Repository staff only until 29 November 2021.
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Accepted/In Press date: 29 May 2020

Identifiers

Local EPrints ID: 441475
URI: http://eprints.soton.ac.uk/id/eprint/441475
ISSN: 1796-2021
PURE UUID: c52cfd57-49b8-41ec-8f38-537892b7e58a

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Date deposited: 15 Jun 2020 16:30
Last modified: 02 Dec 2020 17:34

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